Hi, I’m Jessica Schroeder and today I’ll be presenting highlights from our paper, Examining
Opportunities for Goal-Directed Self-Tracking to Support Chronic Condition Management
So many people and health providers are excited about the prospect of using self-tracking
to better understand and manage chronic health conditions. Self-tracked data has the potential
to help people form a more complete picture of their habits and overall health, and could
therefore lead to better diagnoses and more personalized treatment recommendations.
But the self-tracking tools people currently use to help manage chronic conditions often
fail to support the goals they have for their condition management. For example people often
find self-tracking overwhelming and without purpose, struggling to define appropriate
tracking goals; they also
Find their self-tracked data does not support their questions, struggling to interpret their
data for their goals; they tend to
find their tools fail to support changes in their tracking over time, struggling to adapt
their tracking to their EVOLVING goals; and finally, they
find collaboration with providers difficult, struggling to align patient and provider goals.
These disconnects often prevent people from using personal data to support their goals
And migraine is a good example of how these disconnects affect people with chronic conditions
in practice. Migraine is characterized by unpredictable, intermittent, and poorly-understood
symptoms. Health providers often recommend that people self-track migraine-related data
to try to better manage their symptoms, but migraines can be caused by personalized contributors
that span a wide range of possible domains, including including sleep, diet, mood and
stress, and environmental factors. Contributors also must accumulate to cause symptoms, complicating
and confounding migraine management.
In prior work, we conducted a formative study to examine current methods and tools used
in migraine tracking and management. We identified these distinct types of goals an individual
may have. Each goal has different associated tracking needs, from data collection to subsequent
analysis, but current tools don’t differentiate between potential tracking goals.
So in this study, we wanted to examine how tools could explicitly elicit, represent,
and support an individual’s tracking goals, via a process we’re calling goal-directed
self-tracking. Specifically, our research questions asked how goal-directed self-tracking
tools could help people and their health providers express their multiple, distinct, and evolving
goals, and tailor their self-tracking to those goals
To investigate these questions, we first
Reanalyzed our formative study data to identify goal-specific tracking needs; for example,
someone tracking to monitor symptoms likely needs to track less data and less often than
someone tracking to predict symptoms
We then developed a system that explicitly supports those goals and associated needs,
expressed as a paper prototype. We iterated on the designs in pilot studies with colleagues
and collaborators in human-centered design and engineering, computer science, and medicine
Finally, we conducted semi-structured interviews with people with migraine and health providers,
using the prototypes to investigate how people might use and respond to a goal-directed self-tracking
system.
Our paper details this full development, but today I’m going to give an overview of our
prototype and findings.
The first step in a goal-directed self-tracking system is to add a goal. In our paper prototype,
people first expressed their goal in their own words. They then categorized their goal
as one of the goal types we identified in the previous study. We found that starting
with this explicit goal setting can elicit goals even for people who had not previously
considered them, and can help them understand what goals might be feasible and useful for
them to pursue, both immediately and in the future.
After selecting a goal, the system walks the person through configuring data to track based
on that goal. The person chooses what symptoms, contributors, and treatments to track for
their goal, either manually configuring or selecting from a list of recommendations.
They also select when to track, and how to track each item. Participants said this process
enabled them to configure exactly and only what they wanted to track for their goals,
which could minimize burden and maximize value of the resulting data. The recommendations
also helped people learn more about migraines, though they also encouraged people to track
more data than they would have otherwise
Finally, the prototype also included post-configuration features, like tracking-related functionalities
and goal-specific analyses and visualizations of the resulting data. We found that considering
people’s goals helped them anticipate and plan for all stages of self-tracking. For
example, participants described how their tracking routines and goals might evolve over
time, and considered when and how they could lapse in tracking and still meet their goals.
Both patients and providers also thought the system would support communication and collaboration
To summarize, we developed goal-directed self-tracking to scaffold the process of deciding what,
when, and how to track towards a specific goal, and to analyze and visualize the resulting
data to support that goal. Based on our findings, we think goal-directed self-tracking has the
potential to help both patients and providers better understand and manage chronic conditions
by enabling them to explicitly express and pursue their goals.
And I want to take a second to thank my fantastic coauthors.
Thank you.